Top 5 R resources on COVID-19 Coronavirus
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The Coronavirus is a serious concern around the globe. With its expansion, there are also more and more online resources about it. This article presents a selection of the best R resources on the COVID-19 virus.
This list is by no means exhaustive. I am not aware of all R resources available online about the Coronavirus, so please feel free to let me know in the comments or by contacting me if you believe that another resource (R package, Shiny app, R code, data, etc.) deserves to be on this list.
R Shiny apps
Coronavirus tracker
Developed by John Coene, this Shiny app tracks the spread of the coronavirus, based on three data sources (John Hopkins, Weixin and DXY Data). The Shiny app, built with shinyMobile (which makes it responsive on different screen sizes), presents in a really nice way the number of deaths, confirmed, suspected and recovered cases by time and region.
The code is available on GitHub.
COVID-19 outbreak
Developed by the department of Public Health of the Strasbourg University Hospital and the Laboratory of Biostatistics and Medical Informatics of the Strasbourg Medicine Faculty, this Shiny app shows an interactive map for global monitoring of the infection. It focuses on the evolution of the number of cases per country and for a given period in terms of incidence and prevalence.
The code is available on GitHub.
R packages
{nCov2019}
The {nCov2019} package gives you access to epidemiological data on the coronavirus outbreak.1 The package gives real-time statistics and includes historical data. The vignette explains the main functions and possibilities of the package.
Furthermore, the authors of the package also developed a website with interactive plots and time-series forecasts, which could be useful in informing the public and studying how the virus spread in populous countries.
R code
Analyzing COVID-19 outbreak data with R
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Written by Tim Churches, these two articles (part 1 and part 2) explore the R tools and packages that might be used to analyze the COVID-19 data. Moreover, it presents R code to analyze how contagious is the Coronavirus thanks to the SIR model (an epidemiological model).
COVID-19 Data Analysis with {tidyverse} and {ggplot2}
An analysis of data around the Coronavirus with the {tidyverse} and {ggplot2} packages, for China and world wide.
Both documents are a mix of data cleaning, data processing and visualizations of the confirmed/cured cases and death rates across countries or regions.
Data
Thanks for reading. I hope you will find these R resources on the COVID-19 Coronavirus useful. Feel free to let me know in the comments if I missed one.
As always, if you have a question or a suggestion related to the topic covered in this article, please add it as a comment so other readers can benefit from the discussion. If you find a mistake or bug, you can inform me by raising an issue on GitHub. For all other requests, you can contact me here.
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